Explosive nosocomial outbreak of SARS-CoV-2 in a rehabilitation clinic: the limits of genomics for outbreak reconstruction

Comprehensive contact tracing during an outbreak of alpha-variant SARS-CoV-2 in a rural community reveals less viral genomic diversity and higher household secondary attack rates than expected

Sequencing of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes throughout the COVID-19 pandemic has generated a wealth of data on viral evolution across populations, but only a few studies have so far explored SARS-CoV-2 evolution across large connected transmission networks. Here, we couple data from SARS-CoV-2 sequencing with contact tracing data from an outbreak with a single origin in a rural Norwegian community where samples from all exposed persons were collected prospectively. A total of 134 nasopharyngeal samples were positive by PCR. Among the 121 retrievable genomes, 81 were identical to the genome of the introductor, thus demonstrating that genomics beyond clustering genotypically similar viral genomes to confirm relatedness offers limited additional value to manual contact tracing. In the cases where mutations were discovered, five small genetic clusters were identified. We observed a household secondary attack rate of 77%, with 92% of household members infected among households with secondary transmission, suggesting that SARS-CoV-2 introduction into large families is likely to affect all household members.

IMPORTANCE

In outbreak investigations, obtaining a full overview of infected individuals within a population is seldom achieved. We here present an example where a single introduction of B1.1.7 SARS-CoV-2 within a rural community allowed for tracing of the virus from an introductor via dissemination through larger gatherings into households. The outbreak occurred before widespread vaccination, allowing for a "natural" outbreak development with community lockdown. We show through sequencing that the virus can infect up to five consecutive persons without gaining mutations, thereby showing that contact tracing seems more important than sequencing for local outbreak investigations in settings with few alternative introductory transmission pathways. We also show how larger households where a child introduced transmission appeared more likely to promote further spread of the virus compared to households with an adult as the primary introductor.

SARS-CoV-2 immunity and reasons for non-vaccination among healthcare workers from eastern and northern Switzerland: results from a nested multicentre cross-sectional study

AIMS OF THE STUDY

We aimed to assess the extent of SARS-CoV-2 humoral immunity elicited by previous infections and/or vaccination among healthcare workers, and to identify reasons why healthcare workers decided against vaccination.

METHODS

This nested cross-sectional study included volunteer healthcare workers from 14 healthcare institutions in German-speaking Switzerland. In January 2021, SARS-CoV-2 vaccines were available for healthcare workers. In May and June 2022, participants answered electronic questionnaires regarding baseline characteristics including SARS-CoV-2 vaccination status (with one or more vaccine doses defined as vaccinated) and previous SARS-CoV-2 infections. Unvaccinated participants indicated their reasons for non-vaccination. Participants underwent testing for SARS-CoV-2 anti-spike (anti-S) and anti-nucleocapsid (anti-N) antibodies. Antibody prevalence was described across age groups. In addition, we performed multivariable logistic regression to identify baseline characteristics independently associated with non-vaccination and described reasons for non-vaccination.

RESULTS

Among 22,438 eligible employees, 3,436 (15%) participated; the median age was 43.7 years (range 16-73), 2,794 (81.3%) were female, and 1,407 (47.7%) identified as nurses; 3,414 (99.4%) underwent serology testing, among whom 3,383 (99.0%) had detectable anti-S (3,357, 98.3%) antibodies, anti-N (2,396, 70.1%) antibodies, or both (2,370, 69.4%). A total of 296 (8.6%) healthcare workers were unvaccinated, whereas 3,140 (91.4%) were vaccinated. In multivariable analysis, age (adjusted OR [aOR] 1.02 per year, 95% CI 1.01-1.03), being a physician (aOR 3.22, 95% CI 1.75-5.92) or administrator (aOR 1.88, 95% CI 1.27-2.80), and having higher education (aOR 2.23, 95% CI 1.09-4.57) were positively associated with vaccine uptake, whereas working in non-acute care (aOR 0.58, 95% CI 0.34-0.97), active smoking (aOR 0.68, 95% CI 0.51-0.91), and taking prophylactic home remedies against SARS-CoV-2 (aOR 0.42, 95% CI 0.31-0.56) were negatively associated. Important reasons for non-vaccination were a belief that the vaccine might not have long-lasting immunity (267/291, 92.1%) and a preference for gaining naturally acquired instead of vaccine-induced immunity (241/289, 83.4%).

CONCLUSIONS

Almost all healthcare workers in our cohort had specific antibodies against SARS-CoV-2 from natural infection and/or from vaccination. Young healthcare workers and those working in non-acute settings were less likely to be vaccinated, whereas physicians and administrative staff showed higher vaccination uptake. Presumed ineffectiveness of the vaccine is an important reason for non-vaccination.

Genomic Epidemiology of Severe Acute Respiratory Syndrome Coronavirus 2 in a County Jail

Background

In the coronavirus disease 2019 (COVID-19) pandemic, correctional facilities are potential hotspots for transmission. We examined the genomic epidemiology of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) early in the pandemic in one of the country's largest urban jails.

Methods

Existing SARS-CoV-2 isolates from 131 detainees at the Cook County Jail in Chicago, Illinois, from March 2020 through May 2020 were analyzed by whole-genome sequencing. Contemporaneous isolates from Rush University Medical Center (Chicago, Illinois) and the Global Initiative on Sharing All Influenza Data (GISAID) were used to identify genetic clusters containing only jail isolates. Transmission windows were identified for each pair of detainees using the date of the SARS-CoV-2-positive test and location data to determine if detainees overlapped in the jail, within a specific building, or within particular living units during transmission windows.

Results

We identified 29 jail-only clusters that contained 75 of the 132 SARS-CoV-2 isolates from detainees; of these clusters, 17 (58.6%) had individuals who overlapped in the jail during putative transmission windows. Focusing on specific buildings revealed that 2 buildings, a single- and double-cell style of housing. were associated with having detainees infected with similar SARS-CoV-2 genomes during their infectious time period (P < .001).

Conclusions

Our findings suggest that there was transmission of SARS-CoV-2 in the jail, in the setting of extensive importation of COVID-19 from the community. Numerous infection control practices at intake and during incarceration were implemented in the jail to limit viral spread. Our study shows the importance of genomic analysis in this type of settings and how it can be utilized within infection control protocols.

Coronavirus disease 2019 (COVID-19) outbreak on an in-patient medical unit associated with unrecognized exposures in common areas-Epidemiological and whole-genome sequencing investigation

OBJECTIVE

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) hospital outbreaks have been common and devastating during the coronavirus disease 2019 (COVID-19) pandemic. Understanding SARS-CoV-2 transmission in these environments is critical for preventing and managing outbreaks.

DESIGN

Outbreak investigation through epidemiological mapping and whole-genome sequencing phylogeny.

SETTING

Hospital in-patient medical unit outbreak in Toronto, Canada, from November 2020 to January 2021.

PARTICIPANTS

The outbreak involved 8 patients and 10 staff and was associated with 3 patient deaths.

RESULTS

Patients being cared for in geriatric chairs at the nursing station were at high risk for both acquiring and transmitting SARS-CoV-2 to other patients and staff. Furthermore, given the informal nature of these transmissions, they were not initially recognized, which led to further transmission and missing the opportunity for preventative COVID-19 therapies.

CONCLUSIONS

During outbreak prevention and management, the risk of informal patient care settings, such as geriatric chairs, should be considered. During high-risk periods or during outbreaks, efforts should be made to care for patients in their rooms when possible.

How have mathematical models contributed to understanding the transmission and control of SARS-CoV-2 in healthcare settings? A systematic search and review

Since the onset of the COVID-19 pandemic, mathematical models have been widely used to inform public health recommendations regarding COVID-19 control in healthcare settings. The objective of this study was to systematically review SARS-CoV-2 transmission models in healthcare settings, and to summarize their contributions to understanding nosocomial COVID-19. A systematic search and review of published articles indexed in PubMed was carried out. Modelling studies describing dynamic inter-individual transmission of SARS-CoV-2 in healthcare settings, published by mid-February 2022 were included. Models have mostly focused on acute-care and long-term-care facilities in high-income countries. Models have quantified outbreak risk, showing great variation across settings and pandemic periods. Regarding surveillance, routine testing rather than symptom-based was highlighted as essential for COVID-19 prevention due to high rates of silent transmission. Surveillance impacts depended critically on testing frequency, diagnostic sensitivity, and turn-around time. Healthcare re-organization also proved to have large epidemiological impacts: beyond obvious benefits of isolating cases and limiting inter-individual contact, more complex strategies (staggered staff scheduling, immune-based cohorting) reduced infection risk. Finally, vaccination impact, while highly effective for limiting COVID-19 burden, varied substantially depending on assumed mechanistic impacts on infection acquisition, symptom onset and transmission. Modelling results form an extensive evidence base that may inform control strategies for future waves of SARS-CoV-2 and other viral respiratory pathogens. We propose new avenues for future models of healthcare-associated outbreaks, with the aim of enhancing their efficiency and contributions to decision-making.

SARS-CoV-2 Outbreak Investigation Using Contact Tracing and Whole-Genome Sequencing in an Ontario Tertiary Care Hospital

Genomic epidemiology can facilitate an understanding of evolutionary history and transmission dynamics of a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak. We used next-generation sequencing techniques to study SARS-CoV-2 genomes isolated from patients and health care workers (HCWs) across five wards of a Canadian hospital with an ongoing SARS-CoV-2 outbreak. Using traditional contact tracing methods, we show transmission events between patients and HCWs, which were also supported by the SARS-CoV-2 lineage assignments. The outbreak predominantly involved SARS-CoV-2 B.1.564.1 across all five wards, but we also show evidence of community introductions of lineages B.1, B.1.1.32, and B.1.231, falsely assumed to be outbreak related. Altogether, our study exemplifies the value of using contact tracing in combination with genomic epidemiology to understand the transmission dynamics and genetic underpinnings of a SARS-CoV-2 outbreak. IMPORTANCE Our manuscript describes a SARS-CoV-2 outbreak investigation in an Ontario tertiary care hospital. We use traditional contract tracing paired with whole-genome sequencing to facilitate an understanding of the evolutionary history and transmission dynamics of this SARS-CoV-2 outbreak in a clinical setting. These advancements have enabled the incorporation of phylogenetics and genomic epidemiology into the understanding of clinical outbreaks. We show that genomic epidemiology can help to explore the genetic evolution of a pathogen in real time, enabling the identification of the index case and helping understand its transmission dynamics to develop better strategies to prevent future spread of SARS-CoV-2 in congregate, clinical settings such as hospitals.

Combining epidemiological data and whole genome sequencing to understand SARS-CoV-2 transmission dynamics in a large tertiary care hospital during the first COVID-19 wave in The Netherlands focusing on healthcare workers

BACKGROUND

Healthcare facilities have been challenged by the risk of SARS-CoV-2 transmission between healthcare workers (HCW) and patients. During the first wave of the COVID-19 pandemic, infections among HCW were observed, questioning infection prevention and control (IPC) measures implemented at that time.

AIM

This study aimed to identify nosocomial transmission routes of SARS-CoV-2 between HCW and patients in a tertiary care hospital.

METHODS

All SARS-CoV-2 PCR positive HCW and patients identified between 1 March and 19 May 2020, were included in the analysis. Epidemiological data were collected from patient files and HCW contact tracing interviews. Whole genome sequences of SARS-CoV-2 were generated using Nanopore sequencing (WGS). Epidemiological clusters were identified, whereafter WGS and epidemiological data were combined for re-evaluation of epidemiological clusters and identification of potential transmission clusters. HCW infections were further classified into categories based on the likelihood that the infection was acquired via nosocomial transmission. Secondary cases were defined as COVID-19 cases in our hospital, part of a transmission cluster, of which the index case was either a patient or HCW from our hospital.

FINDINGS

The study population consisted of 293 HCW and 245 patients. Epidemiological data revealed 36 potential epidemiological clusters, with an estimated 222 (75.7%) HCW as secondary cases. WGS results were available for 195 HCW (88.2%) and 20 patients (12.8%) who belonged to an epidemiological cluster. Re-evaluation of the epidemiological clusters, with the available WGS data identified 31 transmission clusters with 65 (29.4%) HCW as secondary cases. Transmission clusters were all part of 18 (50.0%) previously determined epidemiological clusters, demonstrating that several larger outbreaks actually consisted, of several smaller transmission clusters. A total of 21 (7.2%) HCW infections were classified as from confirmed nosocomial, of which 18 were acquired from another HCW and 3 from a patient.

CONCLUSION

The majority of SARS-CoV-2 infections among HCW could be attributed to community-acquired infection. Infections among HCW that could be classified as due to nosocomial transmission, were mainly caused by HCW-to-HCW transmission rather than patient-to-HCW transmission. It is important to recognize the uncertainties of cluster analyses based solely on epidemiological data.

Impact of supplementary air filtration on aerosols and particulate matter in a UK hospital ward: a case study

BACKGROUND

Aerosol spread of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is a major problem in hospitals, leading to an increase in supplementary high-efficiency particulate air filtration aimed at reducing nosocomial transmission. This article reports a natural experiment that occurred when an air cleaning unit (ACU) on a medicine for older people ward was switched off accidentally while being commissioned.

AIM

To assess aerosol transport within the ward and determine whether the ACU reduced airborne particulate matter (PM) levels.

METHODS

An ACU was placed in a ward comprising two six-bedded bays plus three single-bed isolation rooms which had previously experienced several outbreaks of coronavirus disease 2019. During commissioning, real-time measurements of key indoor air quality parameters (PM1-10, CO2, temperature and humidity) were collected from multiple sensors over 2 days. During this period, the ACU was switched off accidentally for approximately 7 h, allowing the impact of the intervention on PM to be assessed.

FINDINGS

The ACU reduced the PM counts considerably (e.g. PM1 65.5-78.2%) throughout the ward (P<0.001 all sizes), with positive correlation found for all PM fractions and CO2 (r=0.343-0.817; all P<0.001). PM counts rose/fell simultaneously when the ACU was off, with correlation of PM signals from multiple locations (e.g. r=0.343-0.868; all P<0.001) for particulates <1 μm).

CONCLUSION

Aerosols migrated rapidly between the various ward subcompartments, suggesting that social distancing alone cannot prevent nosocomial transmission of SARS-CoV-2 as this fails to mitigate longer-range (>2 m) transmission. The ACU reduced PM levels considerably throughout the ward space, indicating its potential as an effective intervention to reduce the risk posed by infectious airborne particles.

Patient and ward related risk factors in a multi-ward nosocomial outbreak of COVID-19: Outbreak investigation and matched case-control study

BACKGROUND

Risk factors for nosocomial COVID-19 outbreaks continue to evolve. The aim of this study was to investigate a multi-ward nosocomial outbreak of COVID-19 between 1st September and 15th November 2020, occurring in a setting without vaccination for any healthcare workers or patients.

METHODS

Outbreak report and retrospective, matched case-control study using incidence density sampling in three cardiac wards in an 1100-bed tertiary teaching hospital in Calgary, Alberta, Canada. Patients were confirmed/probable COVID-19 cases and contemporaneous control patients without COVID-19. COVID-19 outbreak definitions were based on Public Health guidelines. Clinical and environmental specimens were tested by RT-PCR and as applicable quantitative viral cultures and whole genome sequencing were conducted. Controls were inpatients on the cardiac wards during the study period confirmed to be without COVID-19, matched to outbreak cases by time of symptom onset dates, age within ± 15 years and were admitted in hospital for at least 2 days. Demographics, Braden Score, baseline medications, laboratory measures, co-morbidities, and hospitalization characteristics were collected on cases and controls. Univariate and multivariate conditional logistical regression was used to identify independent risk factors for nosocomial COVID-19.

RESULTS

The outbreak involved 42 healthcare workers and 39 patients. The strongest independent risk factor for nosocomial COVID-19 (IRR 3.21, 95% CI 1.47-7.02) was exposure in a multi-bedded room. Of 45 strains successfully sequenced, 44 (97.8%) were B.1.128 and differed from the most common circulating community lineages. SARS-CoV-2 positive cultures were detected in 56.7% (34/60) of clinical and environmental specimens. The multidisciplinary outbreak team observed eleven contributing events to transmission during the outbreak.

CONCLUSIONS

Transmission routes of SARS-CoV-2 in hospital outbreaks are complex; however multi-bedded rooms play a significant role in the transmission of SARS-CoV-2.

Lessons learned: use of WGS in real-time investigation of suspected intrahospital SARS-CoV-2 outbreaks

BACKGROUND

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has been a continuing source of hospital-acquired infection and outbreaks. At Akershus University Hospital in Norway, traditional contact tracing has been combined with whole-genome sequencing (WGS) surveillance in real-time to investigate potential hospital outbreaks.

AIM

To describe the advantages and challenges encountered when using WGS as a real-time tool in hospital outbreak investigation and surveillance during the SARS-CoV-2 pandemic.

METHODS

Routine contact tracing in the hospital was performed for all healthcare workers (HCWs) who tested positive for SARS-CoV-2. Viral RNA from all positive patient and HCW samples was sequenced in real-time using nanopore sequencing and the ARTIC Network protocol. Suspected outbreaks involving five or more individuals with viral sequences were described.

FINDINGS

Nine outbreaks were suspected based on contact tracing, and one outbreak was suspected based on WGS results. Five outbreaks were confirmed; of these, two outbreaks were supported but could not be confirmed by WGS with high confidence, one outbreak was found to consist of two different lineages, and two outbreaks were refuted.

CONCLUSIONS

WGS is a valuable tool in hospital outbreak investigations when combined with traditional contact tracing. Inclusion of WGS data improved outbreak demarcation, identified unknown transmission chains, and highlighted weaknesses in existing infection control measures.

Genomic Investigation to Identify Sources of Severe Acute Respiratory Syndrome Coronavirus 2 Infection Among Healthcare Personnel in an Acute Care Hospital

Background

Identifying the source of healthcare personnel (HCP) coronavirus disease 2019 (COVID-19) is important to guide occupational safety efforts. We used a combined whole genome sequencing (WGS) and epidemiologic approach to investigate the source of HCP COVID-19 at a tertiary-care center early in the COVID-19 pandemic.

Methods

Remnant nasopharyngeal swab samples from HCP and patients with polymerase chain reaction-proven COVID-19 from a period with complete sample retention (14 March 2020 to 10 April 2020) at Rush University Medical Center in Chicago, Illinois, underwent viral RNA extraction and WGS. Genomes with >90% coverage underwent cluster detection using a 2 single-nucleotide variant genetic distance cutoff. Genomic clusters were evaluated for epidemiologic linkages, with strong linkages defined by evidence of time/location overlap.

Results

We analyzed 1031 sequences, identifying 49 clusters that included ≥1 HCP (265 patients, 115 HCP). Most HCP infections were not healthcare associated (88/115 [76.5%]). We did not identify any strong epidemiologic linkages for patient-to-HCP transmission. Thirteen HCP cases (11.3%) were attributed to a potential patient source (weak evidence involving nonclinical staff that lacked location data to prove or disprove contact with patients in same cluster). Fourteen HCP cases (12.2%) were attributed to HCP source (11 with strong evidence).

Conclusions

Using genomic and epidemiologic data, we found that most HCP severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections were not healthcare associated. We did not find strong evidence of patient-to-HCP transmission of SARS-CoV-2.

Repeated transmission of SARS-CoV-2 in an overcrowded Irish emergency department elucidated by whole-genome sequencing

AIM

To provide a detailed genomic-epidemiological description of a complex multi-ward SARS-CoV-2 outbreak, which originated in the crowded emergency department (ED) in our hospital during the third wave of the COVID-19 pandemic, and was elucidated promptly by local whole-genome sequencing (WGS).

METHODS

SARS-CoV-2 was detected by reverse transcriptase real-time polymerase chain reaction on viral RNA extracted from nasopharyngeal swabs. WGS was performed using an Oxford MinION Mk1C instrument following the ARTIC v3 sequencing protocol. High-quality consensus genomes were assembled with the artic-ncov2019 bioinformatics pipeline and viral phylogenetic trees were built, inferred by maximum-likelihood. Clusters were defined using a threshold of 0-1 single nucleotide polymorphisms (SNPs) between epidemiologically linked sequences.

RESULTS

In April 2021, outbreaks of COVID-19 were declared on two wards at University Hospital Limerick after 4 healthcare-associated SARS-CoV-2 infections were detected by post-admission surveillance testing. Contact tracing identified 12 further connected cases; all with direct or indirect links to the ED 'COVID Zone'. All sequences were assigned to the Pangolin B.1.1.7 lineage by WGS, and SNP-level analysis revealed two distinct but simultaneous clusters of infections. Repeated transmission in the ED was demonstrated, involving patients accommodated on trolleys in crowded areas, resulting in multiple generations of infections across three inpatient hospital wards and subsequently to the local community. These findings informed mitigation efforts to prevent cross-transmission in the ED.

CONCLUSION

Cross-transmission of SARS-CoV-2 occurred repeatedly in an overcrowded emergency department. Viral WGS elucidated complex viral transmission networks in our hospital and informed infection, prevention and control practice.

Prediction of hospital-onset COVID-19 infections using dynamic networks of patient contact: an international retrospective cohort study

BACKGROUND

Real-time prediction is key to prevention and control of infections associated with health-care settings. Contacts enable spread of many infections, yet most risk prediction frameworks fail to account for their dynamics. We developed, tested, and internationally validated a real-time machine-learning framework, incorporating dynamic patient-contact networks to predict hospital-onset COVID-19 infections (HOCIs) at the individual level.

METHODS

We report an international retrospective cohort study of our framework, which extracted patient-contact networks from routine hospital data and combined network-derived variables with clinical and contextual information to predict individual infection risk. We trained and tested the framework on HOCIs using the data from 51 157 hospital inpatients admitted to a UK National Health Service hospital group (Imperial College Healthcare NHS Trust) between April 1, 2020, and April 1, 2021, intersecting the first two COVID-19 surges. We validated the framework using data from a Swiss hospital group (Department of Rehabilitation, Geneva University Hospitals) during a COVID-19 surge (from March 1 to May 31, 2020; 40 057 inpatients) and from the same UK group after COVID-19 surges (from April 2 to Aug 13, 2021; 43 375 inpatients). All inpatients with a bed allocation during the study periods were included in the computation of network-derived and contextual variables. In predicting patient-level HOCI risk, only inpatients spending 3 or more days in hospital during the study period were examined for HOCI acquisition risk.

FINDINGS

The framework was highly predictive across test data with all variable types (area under the curve [AUC]-receiver operating characteristic curve [ROC] 0·89 [95% CI 0·88-0·90]) and similarly predictive using only contact-network variables (0·88 [0·86-0·90]). Prediction was reduced when using only hospital contextual (AUC-ROC 0·82 [95% CI 0·80-0·84]) or patient clinical (0·64 [0·62-0·66]) variables. A model with only three variables (ie, network closeness, direct contacts with infectious patients [network derived], and hospital COVID-19 prevalence [hospital contextual]) achieved AUC-ROC 0·85 (95% CI 0·82-0·88). Incorporating contact-network variables improved performance across both validation datasets (AUC-ROC in the Geneva dataset increased from 0·84 [95% CI 0·82-0·86] to 0·88 [0·86-0·90]; AUC-ROC in the UK post-surge dataset increased from 0·49 [0·46-0·52] to 0·68 [0·64-0·70]).

INTERPRETATION

Dynamic contact networks are robust predictors of individual patient risk of HOCIs. Their integration in clinical care could enhance individualised infection prevention and early diagnosis of COVID-19 and other nosocomial infections.

FUNDING

Medical Research Foundation, WHO, Engineering and Physical Sciences Research Council, National Institute for Health Research (NIHR), Swiss National Science Foundation, and German Research Foundation.

Prevention of SARS-CoV-2 and respiratory viral infections in healthcare settings: current and emerging concepts

PURPOSE OF REVIEW

COVID-19 has catalyzed a wealth of new data on the science of respiratory pathogen transmission and revealed opportunities to enhance infection prevention practices in healthcare settings.

RECENT FINDINGS

New data refute the traditional division between droplet vs airborne transmission and clarify the central role of aerosols in spreading all respiratory viruses, including Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), even in the absence of so-called 'aerosol-generating procedures' (AGPs). Indeed, most AGPs generate fewer aerosols than talking, labored breathing, or coughing. Risk factors for transmission include high viral loads, symptoms, proximity, prolonged exposure, lack of masking, and poor ventilation. Testing all patients on admission and thereafter can identify early occult infections and prevent hospital-based clusters. Additional prevention strategies include universal masking, encouraging universal vaccination, preferential use of N95 respirators when community rates are high, improving native ventilation, utilizing portable high-efficiency particulate air filters when ventilation is limited, and minimizing room sharing when possible.

SUMMARY

Multifaceted infection prevention programs that include universal testing, masking, vaccination, and enhanced ventilation can minimize nosocomial SARS-CoV-2 infections in patients and workplace infections in healthcare personnel. Extending these insights to other respiratory viruses may further increase the safety of healthcare and ready hospitals for novel respiratory viruses that may emerge in the future.

Reconstruction of transmission chains of SARS-CoV-2 amidst multiple outbreaks in a geriatric acute-care hospital: a combined retrospective epidemiological and genomic study

Background

There is ongoing uncertainty regarding transmission chains and the respective roles of healthcare workers (HCWs) and elderly patients in nosocomial outbreaks of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in geriatric settings.

Methods

We performed a retrospective cohort study including patients with nosocomial coronavirus disease 2019 (COVID-19) in four outbreak-affected wards, and all SARS-CoV-2 RT-PCR positive HCWs from a Swiss university-affiliated geriatric acute-care hospital that admitted both Covid-19 and non-Covid-19 patients during the first pandemic wave in Spring 2020. We combined epidemiological and genetic sequencing data using a Bayesian modelling framework, and reconstructed transmission dynamics of SARS-CoV-2 involving patients and HCWs, to determine who infected whom. We evaluated general transmission patterns according to case type (HCWs working in dedicated Covid-19 cohorting wards: HCWcovid; HCWs working in non-Covid-19 wards where outbreaks occurred: HCWoutbreak; patients with nosocomial Covid-19: patientnoso) by deriving the proportion of infections attributed to each case type across all posterior trees and comparing them to random expectations.

Results

During the study period (1 March to 7 May 2020), we included 180 SARS-CoV-2 positive cases: 127 HCWs (91 HCWcovid, 36 HCWoutbreak) and 53 patients. The attack rates ranged from 10% to 19% for patients, and 21% for HCWs. We estimated that 16 importation events occurred with high confidence (4 patients, 12 HCWs) that jointly led to up to 41 secondary cases; in six additional cases (5 HCWs, 1 patient), importation was possible with a posterior probability between 10% and 50%. Most patient-to-patient transmission events involved patients having shared a ward (95.2%, 95% credible interval [CrI] 84.2%-100%), in contrast to those having shared a room (19.7%, 95% CrI 6.7%-33.3%). Transmission events tended to cluster by case type: patientnoso were almost twice as likely to be infected by other patientnoso than expected (observed:expected ratio 2.16, 95% CrI 1.17-4.20, p=0.006); similarly, HCWoutbreak were more than twice as likely to be infected by other HCWoutbreak than expected (2.72, 95% CrI 0.87-9.00, p=0.06). The proportion of infectors being HCWcovid was as expected as random. We found a trend towards a greater proportion of high transmitters (≥2 secondary cases) among HCWoutbreak than patientnoso in the late phases (28.6% vs. 11.8%) of the outbreak, although this was not statistically significant.

Conclusions

Most importation events were linked to HCW. Unexpectedly, transmission between HCWcovid was more limited than transmission between patients and HCWoutbreak. This finding highlights gaps in infection control and suggests the possible areas of improvements to limit the extent of nosocomial transmission.

Funding

This study was supported by a grant from the Swiss National Science Foundation under the NRP78 funding scheme (Grant no. 4078P0_198363).

Transmission visualizations of healthcare infection clusters: A scoping review

OBJECTIVE

To evaluate infectious pathogen transmission data visualizations in outbreak publications.

DESIGN

Scoping review.

METHODS

Medline was searched for outbreak investigations of infectious diseases within healthcare facilities that included ≥1 data visualization of transmission using data observable by an infection preventionist showing temporal and/or spatial relationships. Abstracted data included the nature of the cluster(s) (pathogen, scope of transmission, and individuals involved) and data visualization characteristics including visualization type, transmission elements, and software.

RESULTS

From 1,957 articles retrieved, we analyzed 30 articles including 37 data visualizations. The median cluster size was 20.5 individuals (range, 7-1,963) and lasted a median of 214 days (range, 12-5,204). Among the data visualization types, 10 (27%) were floor-plan transmission maps, 6 (16%) were timelines, 11 (30%) were transmission networks, 3 (8%) were Gantt charts, 4 (11%) were cluster map, and 4 (11%) were other types. In addition, 26 data visualizations (70%) contained spatial elements, 26 (70%) included person type, and 19 (51%) contained time elements. None of the data visualizations contained contagious periods and only 2 (5%) contained symptom-onset date.

CONCLUSIONS

The data visualizations of healthcare-associated infectious disease outbreaks in the systematic review were diverse in type and visualization elements, though no data visualization contained all elements important to deriving hypotheses about transmission pathways. These findings aid in understanding the visualizing transmission pathways by describing essential elements of the data visualization and will inform the creation of a standardized mapping tool to aid in earlier initiation of interventions to prevent transmission.

Occupational and community risk of SARS-CoV-2 infection among employees of a long-term care facility: an observational study

BACKGROUND

We investigated the contribution of both occupational and community exposure for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection among employees of a university-affiliated long-term care facility (LTCF), during the 1st pandemic wave in Switzerland (March-June 2020).

METHODS

We performed a nested analysis of a seroprevalence study among all volunteering LTCF staff to determine community and nosocomial risk factors for SARS-CoV-2 seropositivity using modified Poison regression. We also combined epidemiological and genetic sequencing data from a coronavirus disease 2019 (COVID-19) outbreak investigation in a LTCF ward to infer transmission dynamics and acquisition routes of SARS-CoV-2, and evaluated strain relatedness using a maximum likelihood phylogenetic tree.

RESULTS

Among 285 LTCF employees, 176 participated in the seroprevalence study, of whom 30 (17%) were seropositive for SARS-CoV-2. Most (141/176, 80%) were healthcare workers (HCWs). Risk factors for seropositivity included exposure to a COVID-19 inpatient (adjusted prevalence ratio [aPR] 2.6; 95% CI 0.9-8.1) and community contact with a COVID-19 case (aPR 1.7; 95% CI 0.8-3.5). Among 18 employees included in the outbreak investigation, the outbreak reconstruction suggests 4 likely importation events by HCWs with secondary transmissions to other HCWs and patients.

CONCLUSIONS

These two complementary epidemiologic and molecular approaches suggest a substantial contribution of both occupational and community exposures to COVID-19 risk among HCWs in LTCFs. These data may help to better assess the importance of occupational health hazards and related legal implications during the COVID-19 pandemic.

Characterising within-hospitalSARS-CoV-2 transmission events using epidemiological and viral genomic data across two pandemic waves

Hospital outbreaks of COVID19 result in considerable mortality and disruption to healthcare services and yet little is known about transmission within this setting. We characterise within hospital transmission by combining viral genomic and epidemiological data using Bayesian modelling amongst 2181 patients and healthcare workers from a large UK NHS Trust. Transmission events were compared between Wave 1 (1st March to 25th J'uly 2020) and Wave 2 (30th November 2020 to 24th January 2021). We show that staff-to-staff transmissions reduced from 31.6% to 12.9% of all infections. Patient-to-patient transmissions increased from 27.1% to 52.1%. 40%-50% of hospital-onset patient cases resulted in onward transmission compared to 4% of community-acquired cases. Control measures introduced during the pandemic likely reduced transmissions between healthcare workers but were insufficient to prevent increasing numbers of patient-to-patient transmissions. As hospital-acquired cases drive most onward transmission, earlier identification of nosocomial cases will be required to break hospital transmission chains.

Local Emergence of A del HV69-70 SARS-CoV-2 Variant in Burgundy, France

In the autumn of 2020, a short-lived epidemic of a spike del69-70 deletion variant of SARS-CoV-2 was identified, with most cases (n = 95) found in Montceau-les-Mines, France. This spike gene target failure (SGTF) variant spread quickly in nursing homes. The Alpha variant, which also harbors this deletion, appeared in Burgundy in January 2021 after the disappearance of the Montceau-les-Mines del69-70 variant. Our findings illustrate the risk of the fast spread of geographically isolated variants and reinforce the need for the continuous tracking of outbreaks. In some cases, these studies may reveal emerging variants that affect public health or vaccine development.

New Insights into the Prevention of Hospital-Acquired Pneumonia/Ventilator-Associated Pneumonia Caused by Viruses

A fifth or more of hospital-acquired pneumonias may be attributable to respiratory viruses. The SARS-CoV-2 pandemic has clearly demonstrated the potential morbidity and mortality of respiratory viruses and the constant threat of nosocomial transmission and hospital-based clusters. Data from before the pandemic suggest the same can be true of influenza, respiratory syncytial virus, and other respiratory viruses. The pandemic has also helped clarify the primary mechanisms and risk factors for viral transmission. Respiratory viruses are primarily transmitted by respiratory aerosols that are routinely emitted when people exhale, talk, and cough. Labored breathing and coughing increase aerosol generation to a much greater extent than intubation, extubation, positive pressure ventilation, and other so-called aerosol-generating procedures. Transmission risk is proportional to the amount of viral exposure. Most transmissions take place over short distances because respiratory emissions are densest immediately adjacent to the source but then rapidly dilute and diffuse with distance leading to less viral exposure. The primary risk factors for transmission then are high viral loads, proximity, sustained exposure, and poor ventilation as these all increase net viral exposure. Poor ventilation increases the risk of long-distance transmission by allowing aerosol-borne viruses to accumulate over time leading to higher levels of exposure throughout an enclosed space. Surgical and procedural masks reduce viral exposure but do not eradicate it and thus lower but do not eliminate transmission risk. Most hospital-based clusters have been attributed to delayed diagnoses, transmission between roommates, and staff-to-patient infections. Strategies to prevent nosocomial respiratory viral infections include testing all patients upon admission, preventing healthcare providers from working while sick, assuring adequate ventilation, universal masking, and vaccinating both patients and healthcare workers.

Risk factors for nosocomial SARS-CoV-2 infections in patients: results from a retrospective matched case-control study in a tertiary care university center

BACKGROUND

Factors contributing to the spread of SARS-CoV-2 outside the acute care hospital setting have been described in detail. However, data concerning risk factors for nosocomial SARS-CoV-2 infections in hospitalized patients remain scarce. To close this research gap and inform targeted measures for the prevention of nosocomial SARS-CoV-2 infections, we analyzed nosocomial SARS-CoV-2 cases in our hospital during a defined time period.

METHODS

Data on nosocomial SARS-CoV-2 infections in hospitalized patients that occurred between May 2020 and January 2021 at Charité university hospital in Berlin, Germany, were retrospectively gathered. A SARS-CoV-2 infection was considered nosocomial if the patient was admitted with a negative SARS-CoV-2 reverse transcription polymerase chain reaction test and subsequently tested positive on day five or later. As the incubation period of SARS-CoV-2 can be longer than five days, we defined a subgroup of "definite" nosocomial SARS-CoV-2 cases, with a negative test on admission and a positive test after day 10, for which we conducted a matched case-control study with a one to one ratio of cases and controls. We employed a multivariable logistic regression model to identify factors significantly increasing the likelihood of nosocomial SARS-CoV-2 infections.

RESULTS

A total of 170 patients with a nosocomial SARS-CoV-2 infection were identified. The majority of nosocomial SARS-CoV-2 patients (n = 157, 92%) had been treated at wards that reported an outbreak of nosocomial SARS-CoV-2 cases during their stay or up to 14 days later. For 76 patients with definite nosocomial SARS-CoV-2 infections, controls for the case-control study were matched. For this subgroup, the multivariable logistic regression analysis revealed documented contact to SARS-CoV-2 cases (odds ratio: 23.4 (95% confidence interval: 4.6-117.7)) and presence at a ward that experienced a SARS-CoV-2 outbreak (odds ratio: 15.9 (95% confidence interval: 2.5-100.8)) to be the principal risk factors for nosocomial SARS-CoV-2 infection.

CONCLUSIONS

With known contact to SARS-CoV-2 cases and outbreak association revealed as the primary risk factors, our findings confirm known causes of SARS-CoV-2 infections and demonstrate that these also apply to the acute care hospital setting. This underscores the importance of rapidly identifying exposed patients and taking adequate preventive measures.

OUP accepted manuscript

Novel coronavirus disease infection (coronavirus disease 2019, COVID-19) was declared a global pandemic in March 2020 and since then has become a major public health problem. The prevalence of COVID-19 infection and acute kidney injury (AKI) is variable depending on several factors such as race/ethnicity and severity of illness. The pathophysiology of renal involvement in COVID-19 infection is not entirely clear, but it could be in part explained by the viral tropism in the kidney parenchyma. AKI in COVID-19 infection can be either by direct invasion of the virus or as a consequence of immunologic response. Diverse studies have focused on the effect of COVID-19 on glomerulonephritis (GN) patients or the ‘novo’ GN; however, the effect of COVID-19 in acute tubulointerstitial nephritis (ATIN) has been scarcely studied. In this article, we present five cases with different spectrums of COVID-19 infection and ATIN that may suggest that recent diagnosis of ATIN is accompanied by a worse clinical prognosis in comparison with long-term diagnosed ATIN.

OUP accepted manuscript

A detailed understanding of how and when severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission occurs is crucial for designing effective prevention measures. Other than contact tracing, genome sequencing provides information to help infer who infected whom. However, the effectiveness of the genomic approach in this context depends on both (high enough) mutation and (low enough) transmission rates. Today, the level of resolution that we can obtain when describing SARS-CoV-2 outbreaks using just genomic information alone remains unclear. In order to answer this question, we sequenced forty-nine SARS-CoV-2 patient samples from ten local clusters in NW Spain for which partial epidemiological information was available and inferred transmission history using genomic variants. Importantly, we obtained high-quality genomic data, sequencing each sample twice and using unique barcodes to exclude cross-sample contamination. Phylogenetic and cluster analyses showed that consensus genomes were generally sufficient to discriminate among independent transmission clusters. However, levels of intrahost variation were low, which prevented in most cases the unambiguous identification of direct transmission events. After filtering out recurrent variants across clusters, the genomic data were generally compatible with the epidemiological information but did not support specific transmission events over possible alternatives. We estimated the effective transmission bottleneck size to be one to two viral particles for sample pairs whose donor–recipient relationship was likely. Our analyses suggest that intrahost genomic variation in SARS-CoV-2 might be generally limited and that homoplasy and recurrent errors complicate identifying shared intrahost variants. Reliable reconstruction of direct SARS-CoV-2 transmission based solely on genomic data seems hindered by a slow mutation rate, potential convergent events, and technical artifacts. Detailed contact tracing seems essential in most cases to study SARS-CoV-2 transmission at high resolution.

Association between SARS-CoV-2 Seroprevalence in Nursing Home Staff and Resident COVID-19 Cases and Mortality: A Cross-Sectional Study

The burden of COVID-19 has disproportionately impacted the elderly, who are at increased risk of severe disease, hospitalization, and death. This cross-sectional study aimed to assess the association between SARS-CoV-2 seroprevalence among nursing home staff, and cumulative incidence rates of COVID-19 cases, hospitalizations, and deaths among residents. Staff seroprevalence was estimated within the SEROCoV-WORK+ study between May and September 2020 across 29 nursing homes in Geneva, Switzerland. Data on nursing home residents were obtained from the canton of Geneva for the period between March and August 2020. Associations were assessed using Spearman’s correlation coefficient and quasi-Poisson regression models. Overall, seroprevalence among staff ranged between 0 and 31.4%, with a median of 8.3%. A positive association was found between staff seroprevalence and resident cumulative incidence of COVID-19 cases (correlation coefficient R = 0.72, 95%CI 0.45–0.87; incidence rate ratio [IRR] = 1.10, 95%CI 1.07–1.17), hospitalizations (R = 0.59, 95%CI 0.25–0.80; IRR = 1.09, 95%CI 1.05–1.13), and deaths (R = 0.71, 95%CI 0.44–0.86; IRR = 1.12, 95%CI 1.07–1.18). Our results suggest that SARS-CoV-2 transmission between staff and residents may contribute to the spread of the virus within nursing homes. Awareness among nursing home professionals of their likely role in the spread of SARS-CoV-2 has the potential to increase vaccination coverage and prevent unnecessary deaths due to COVID-19.